class_docstr: Sharding spec for a tensor, it contains info of the logical device mesh this tensor belong
  to, the entire shape of the tensor before sharded, and the sharding sequence looks like
  [R, R, S0, S1].
  
  Argument:
      device_mesh(DeviceMesh): A logical view of a physical mesh.
      entire_shape(torch.Size): The entire shape of tensor before sharded.
      dim_partition_dict(Dict[int, List[int]]， optional): The key is the dimension of tensor to be sharded,
          and the value of the key describe which logical axis will be sharded in that dimension.
      sharding_sequence(List[_DimSpec], optional): A straight view of ShardingSpec looks like [R, R, S0, S1].
code: def __repr__(self):
      res_list = ["DistSpec:"]
      res_list.append(
          f"\n\tshard_sequence: "
          + ",".join(str(dimspec) for dimspec in self.sharding_sequence)
      )
      res_list.append(
          f"\n\tdevice_mesh_shape: {self.device_mesh.shape}"
      )
      return " ".join(res_list)
  
imports: import operator
  from copy import deepcopy
  from functools import reduce
  import torch
  from colossalai.device.device_mesh import DeviceMesh
  from .utils import merge_same_dim_mesh_list
label_desc: Use image node_id 2 for calling the ShardingSpec obj's underlying member method code with example usage: obj.__repr__() and returns: str